Bridging Quantum Trainability and Autonomous Control: A Comparative Literature Survey of Hybrid Quantum-Classical Optimization in NISQ-Era Machine Learning

Abstract

The emergence of Noisy Intermediate-Scale Quantum (NISQ) hardware has catalyzed two convergent yet methodologically distinct research frontiers: the application of reinforcement learning (RL) to autonomous quantum control, and the integration of parameterized quantum circuits (PQCs) into classical large language model (LLM) fine-tuning pipelines. The trainability of parameterized quantum components under realistic hardware noise and high-dimensional optimization landscapes impacts the feasibility of deploying quantum algorithms in practical settings, helping readers grasp the real-world significance of these advances. Quantum Machine Learning presents a rigorous, empirically grounded benchmark of three RL paradigms — Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Reinforcement Learning from Demonstration (RLFD) — applied to quantum gate synthesis, state preparation, and qubit readout waveform optimization. Ada-QFT (Adaptive Quantum Fine-Tuning) framework, an architecturally novel hybrid system in which a PQC layer is embedded within a pretrained LLM backbone, and whose initialization is governed by a generative AI-driven, Expected Improvement (EI)-guided iterative search supported by a formal sub martingale convergence guarantee. The theoretical, methodological, and empirical terrains covered by these two publications are unified in this literature review. It analyzes the places where the two texts agree and disagree, finds common assumptions and epistemological conflicts, and lays out the blanks that a possible proposed techniques could fill. It goes on to suggest suitable comparative parameters, quantifiable sub-metrics, and potential quantum algorithms to fortify a cohesive scholarly contribution in both fields.